Department of Sports Sciences, Nord University, 7600 Levanger, Norway.
Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland 1010, New Zealand.
Sensors (Basel). 2021 Mar 25;21(7):2288. doi: 10.3390/s21072288.
Injuries in handball are common due to the repetitive demands of overhead throws at high velocities. Monitoring workload is crucial for understanding these demands and improving injury-prevention strategies. However, in handball, it is challenging to monitor throwing workload due to the difficulty of counting the number, intensity, and type of throws during training and competition. The aim of this study was to investigate if an inertial measurement unit (IMU) and machine learning (ML) techniques could be used to detect different types of team handball throws and predict ball velocity. Seventeen players performed several throws with different wind-up (circular and whip-like) and approach types (standing, running, and jumping) while wearing an IMU on their wrist. Ball velocity was measured using a radar gun. ML models predicted peak ball velocity with an error of 1.10 m/s and classified approach type and throw type with 80-87% accuracy. Using IMUs and ML models may offer a practical and automated method for quantifying throw counts and classifying the throw and approach types adopted by handball players.
由于头顶投掷的高速度和重复性要求，手球运动中常见受伤。监测工作量对于了解这些要求和改进预防受伤策略至关重要。然而，在手球中，由于难以在训练和比赛中计算投掷的次数、强度和类型，因此很难监测投掷工作量。本研究的目的是调查惯性测量单元 (IMU) 和机器学习 (ML) 技术是否可用于检测不同类型的团队手球投掷动作并预测球速。十七名运动员在手腕上佩戴 IMU 的情况下，进行了不同的预摆（圆形和鞭状）和接近方式（站立、跑动和跳跃）的多次投掷。使用雷达枪测量球速。ML 模型预测峰值球速的误差为 1.10 米/秒，对接近方式和投掷类型的分类准确率为 80-87%。使用 IMU 和 ML 模型可能为量化投掷次数和对手球运动员采用的投掷和接近方式进行分类提供一种实用且自动化的方法。
